CN118135139B - Obstacle calibration method, obstacle calibration device, electronic equipment and storage medium - Google Patents
Obstacle calibration method, obstacle calibration device, electronic equipment and storage medium Download PDFInfo
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Abstract
The invention relates to an obstacle calibration method, an obstacle calibration device, electronic equipment and a storage medium, wherein the method comprises the following steps: reading a moving track of the mobile station for calibrating the obstacle and a position of the moving track, wherein the position comprises longitude, latitude and height, the mobile station is an unmanned plane, and the moving track is a centimeter-level precision flying track obtained by an onboard satellite navigation RTK when the unmanned plane flies around the external contour of the obstacle; extracting a plurality of feature points along the running track, wherein the intervals among the feature points are larger than a first threshold value; and constructing a three-dimensional obstacle model calibrated with position data based on the plurality of characteristic points. The three-dimensional model of the obstacle constructed by the method can embody the position of the obstacle and the outline characteristics of the obstacle, thereby providing the high-resolution remote sensing map with sufficient obstacle information, realizing more accurate calibration of the obstacle and meeting the requirements of general aviation safety flight scenes.
Description
Technical Field
The present invention relates to the field of positioning technologies, and in particular, to a method and apparatus for calibrating an obstacle, an electronic device, and a storage medium.
Background
General aviation refers to civil aviation activities other than using civil aircraft for public transportation, and is increasingly widely applied to various fields such as agriculture, grazing, forestry, transportation, scientific research and the like along with gradual opening of a low-altitude airspace. Meanwhile, the general aviation industry covers strategic emerging industry systems of the whole industry chains of general aviation research and development and manufacturing, market operation, comprehensive guarantee, extension service and the like, so that the general aviation development is greatly promoted, and the method has important significance for economic development and civil aviation construction.
The general aviation is influenced by factors such as low flying height, complex conditions of an operation area and the like, and a complete general aviation service guarantee system is needed to support, so that services such as a flight plan, aviation information, weather information and the like are provided for general aviation operation. The obstacle information directly affects the general aviation flight safety, and is important content in the navigation service guarantee system. In order to enable the aircraft to accurately avoid the low-altitude obstacle in the flight process, high-precision measurement and calibration of the position and the elevation information of the low-altitude obstacle on the aircraft route are required.
For the height Cheng Ceding of the low-altitude obstacle, along with the development of technology, people slowly explore a convenient and quick measuring method, such as height measurement by adopting a laser altimeter, a millimeter wave altimeter, gravity height measuring equipment and the like. With the continuous development of remote sensing technology, the spatial resolution of the image is continuously improved, and the expression of the ground feature information in the high-resolution image is rich and clear, so that the high-resolution remote sensing image becomes one of important data sources for extracting the ground feature information. The expert students can rapidly acquire the elevation information of the ground through the image data of the high-resolution satellites and the corresponding algorithm processing, so as to acquire the elevation information of the obstacle. However, the altitude information of the low-altitude obstacle, which is obtained only through the high-resolution satellite, cannot meet the application scene of the general aviation in the accuracy.
In summary, the measurement accuracy of the position and the elevation of the traditional low-altitude obstacle is difficult to meet the application scene of the general aviation.
Disclosure of Invention
Based on this, it is necessary to provide an obstacle calibration method, device, electronic equipment and storage medium for solving the problem that the measurement accuracy of the conventional low-altitude obstacle position and elevation is difficult to meet the general aviation application scene.
The invention provides an obstacle calibration method, which comprises the following steps:
Reading a moving track of a mobile station for calibrating an obstacle and a position of the moving track, wherein the position comprises longitude, latitude and height, the mobile station is an unmanned aerial vehicle, and the moving track is a centimeter-level precision flying track obtained by an onboard satellite navigation RTK when the unmanned aerial vehicle flies around the external contour of the obstacle;
Extracting a plurality of characteristic points along the running track, wherein the intervals among the plurality of characteristic points are larger than a first threshold value;
and constructing an obstacle three-dimensional model calibrated with position data based on the plurality of characteristic points.
In one embodiment, the extracting a plurality of feature points along the moving track includes:
selecting an identification point along the running track at each interval of preset time length;
In response to the position of the current identification point being greater than the second threshold from the position of the previous identification point, temporarily storing the current identification point;
When the number of the temporarily stored identification points reaches a third threshold value, marking the currently temporarily stored identification points as suspected feature points;
And confirming that the current suspected feature point is a feature point in response to the fact that the position of the current suspected feature point is greater than the first threshold from the position of the previous feature point.
In one embodiment, the confirming that the current suspected feature point is a feature point includes:
and acquiring the position of each characteristic point in response to the number of the characteristic points reaching a fourth threshold.
In one embodiment, the extracting a plurality of feature points along the moving track further includes:
Clearing the temporarily stored identification point in response to the position of the current identification point being not greater than the second threshold value from the position of the previous identification point;
And in response to the fact that the position of the current suspected feature point is not greater than the first threshold value from the position of the previous feature point, the current suspected feature point is considered to be a non-feature point.
In one embodiment, the constructing the three-dimensional obstacle model calibrated with position data based on the plurality of feature points and the positions of the feature points includes:
selecting characteristic points from the plurality of characteristic points to construct a convex polyhedron;
discarding feature points falling inside the convex polyhedron;
and obtaining the three-dimensional obstacle model calibrated with the position data based on the positions of the characteristic points of the constructed convex polyhedron.
In one embodiment, the selecting the feature points among the plurality of feature points to construct the convex polyhedron includes:
acquiring a feature point with the largest height from the feature points;
the intersection point of the vertical line and the ground is obtained by making the vertical line from the feature point with the maximum height to the ground;
selecting three feature points farthest from the intersection point from the feature points except the feature point with the largest height;
constructing an initial convex polyhedron based on the feature point with the largest height and the three feature points;
And sequentially adding a characteristic point based on the initial convex polyhedron to form a new convex polyhedron until all the characteristic points are added to obtain a final convex polyhedron.
In one embodiment, the method further comprises:
And connecting the characteristic points of the two obstacle three-dimensional models to form a new obstacle three-dimensional model in response to the distance between any characteristic point on the obstacle three-dimensional model and any characteristic point on the other obstacle three-dimensional model being smaller than a fifth threshold value.
The invention also provides an obstacle calibration device, which comprises:
The mobile station is an unmanned aerial vehicle, and the running track is a centimeter-level flight track obtained by an onboard satellite navigation RTK when the unmanned aerial vehicle flies around the external outline of the obstacle;
The extraction module is used for extracting a plurality of characteristic points along the running track, and the intervals among the characteristic points are larger than a first threshold value;
and the construction module is used for constructing an obstacle three-dimensional model calibrated with position data based on the plurality of characteristic points.
The invention also provides electronic equipment, which comprises a memory and a processor, wherein the memory stores a computer program, and the processor realizes the obstacle calibration method according to any one of the above when executing the computer program.
The invention also provides a computer readable storage medium storing a computer program which when executed by a processor implements the obstacle calibration method as described in any one of the above.
According to the method, the device and the electronic equipment for calibrating the obstacle, namely the storage medium, the moving track of the moving station for calibrating the obstacle and the position of the moving track are read, a plurality of characteristic points with intervals larger than a first threshold value are extracted from the moving track, and then the extracted characteristic points are utilized to construct an obstacle three-dimensional model for calibrating obstacle position data.
Drawings
In order to more clearly illustrate the invention or the technical solutions of the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are some embodiments of the invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of an obstacle calibration method according to the present invention;
FIG. 2 is a second flow chart of the method for calibrating an obstacle according to the present invention;
FIG. 3 is a third flow chart of the method for calibrating an obstacle according to the present invention;
Fig. 4 is a plan view of a trajectory of a drone according to a specific embodiment of the present invention;
Fig. 5 is a schematic view of longitude, latitude and altitude of the trajectory of the unmanned aerial vehicle of fig. 4;
FIG. 6 is a three-dimensional model of an obstacle according to an embodiment of the invention;
FIG. 7 is a schematic diagram of an obstacle alignment apparatus of the present invention;
fig. 8 is an internal structural diagram of a computer device of one embodiment.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The following describes a method, an apparatus, an electronic device and a storage medium for calibrating an obstacle in accordance with the present invention with reference to fig. 1 to 8.
As shown in fig. 1, in one embodiment, a method for calibrating an obstacle includes the steps of:
step S110, reading a moving track of the mobile station for calibrating the obstacle and the position of the moving track, wherein the position comprises longitude, latitude and height, the mobile station is an unmanned aerial vehicle, and the moving track is a centimeter-level precision flying track obtained by an onboard satellite navigation RTK when the unmanned aerial vehicle flies around the outer contour of the obstacle.
The navigation airport obstacle calibration adopts unmanned aerial vehicle and RTK (Real-TIME KINEMATIC, real-time dynamic) positioning technology with GPS (Global Positioning System ) -BDS (Beidou Navigation SATELLITE SYSTEM, beidou satellite navigation system) functions. The phase and pseudorange observations equations for satellite j for two receivers, an epoch base station (RTK base station) and a mobile station (here, an drone), can be written as:
In the subscript Which means that the mobile station is provided with a communication channel,Which means that the base station,AndThe pseudorange and carrier phase measurements respectively,Is the wavelength (unit: m),Representing the geometrical distance (in m) of the satellite and the receiver (here the receiver on the drone),Is the tropospheric delay (unit: s),Representing the receiver clock difference (unit: s),Is the satellite clock difference (unit: s),Is the degree of ambiguity of the whole cycle,AndRepresenting pseudorange and carrier phase measurement errors, respectively.
Select the first with the highest elevation angleThe satellite is used as a reference satellite, carrier phases and pseudo-range differences of a mobile station, a base station and the reference satellite and other satellites are formed into double differences, and the double differences can be written as follows:
In the subscript Indicating mobile station minus base station measurements, subscriptRepresent the firstSubtracting the first satelliteMeasurements of a satellite (reference satellite). In the case of a short baseline for the mobile station and base station, the ionospheric and tropospheric delays can be considered equal, so the two formulas above can be written as:
for the baseline vector Expanding and retaining a first item:
In the method, in the process of the invention, From the mobile station to the firstA line of sight vector for the satellites.
Specifically, the unmanned aerial vehicle should fly as close to the obstacle as possible, and should select a plurality of protruding points on the obstacle, which can approximately reflect the outline of the obstacle. In order to reduce the calculation amount of the data processing process of the follow-up unmanned aerial vehicle running track, positioning can be carried out once every certain distance or time length of unmanned aerial vehicle flight, and the positioning position comprises longitude, latitude and altitude. The actual flight distance of the unmanned aerial vehicle has a certain distance, can form measuring error, can record the actual distance of unmanned aerial vehicle when flying apart from the obstacle on the one hand, then offset when calculating and reduce measuring error, on the other hand, need distance obstacle several meters when navigation aircraft flies, therefore this measuring error can be ignored.
Step S120, extracting a plurality of feature points along the running track, wherein the interval between the feature points is larger than a first threshold value.
Specifically, the moving track can reflect the rough outline of the obstacle, and feature points are extracted from the moving track at intervals or for a certain period of time, so that the model of the obstacle can be constructed by using smaller calculated amount. For example, when extracting the feature points, the feature points may be extracted once every interval of a first threshold value, where the first threshold value is converted into a trajectory of 50 centimeters where the unmanned aerial vehicle actually flies.
Step S130, constructing a three-dimensional barrier model calibrated with position data based on the plurality of characteristic points.
Specifically, after the feature points are extracted, the positions of the feature points can be obtained according to the positioning of the unmanned aerial vehicle during flight, the construction of the obstacle outline is completed according to the extracted feature points, and then the actual longitude, latitude and height of the obstacle and the characteristics of the obstacle outline can be accurately obtained according to the positions of the feature points on the obstacle outline, so that the construction of the obstacle three-dimensional model marked with the position data is completed. Meanwhile, modeling data such as the position, the outline size and the like of the obstacle can be packaged, so that the subsequent high-resolution remote sensing map, navigation map and the like can be conveniently used for calibrating the obstacle.
According to the obstacle calibration method, the moving track and the position of the moving track of the obstacle are read, the moving track is calibrated, a plurality of characteristic points with intervals larger than the first threshold value are extracted, the extracted characteristic points are used for constructing an obstacle three-dimensional model with obstacle position data, the position (including longitude, latitude and height of the obstacle) of the obstacle and the outline characteristics of the obstacle can be reflected, so that sufficient obstacle information can be provided for a high-resolution remote sensing map, accurate measurement of the obstacle is realized, accurate obstacle information is provided for general aviation flight, and the requirements of general aviation safety flight scenes are met.
As shown in fig. 2, in one embodiment, extracting a plurality of feature points along a moving trajectory includes the steps of:
step S121, selecting an identification point along the running track at each preset time interval.
Specifically, the preset duration may be 1 second, 2 seconds, or other durations. The running track is converted into an actual flight track of the unmanned aerial vehicle, and when the unmanned aerial vehicle flies at a preset speed, one point is selected as an identification point at intervals of a preset time length.
Step S122, in response to the position of the current identification point being greater than the second threshold from the position of the previous identification point, the current identification point is temporarily stored.
Specifically, in response to the position of the current identification point being not greater than a second threshold from the position of the previous identification point, the registered identification point is cleared. That is, when the unmanned aerial vehicle does not fly forward along the obstacle or spirals in situ, the distance between the current identification point and the previous identification point is less than or equal to the second threshold, and the temporary identification point needs to be cleared at this time, so as to avoid the increase of the calculation burden caused by excessive useless identification points.
And step S123, when the number of the temporarily stored identification points reaches a third threshold value, marking the currently temporarily stored identification points as suspected feature points.
Specifically, when the unmanned aerial vehicle flies, a marking point is generated every time the unmanned aerial vehicle flies for a preset time, and when the distance between the marking point and the previous marking point is larger than a second threshold value, the marking point is continuously temporarily stored, and when the number of the marking points reaches a third threshold value, it is indicated that the unmanned aerial vehicle can fly forwards along an obstacle for a certain distance, and at the moment, the current marking point needs to be marked as a suspected characteristic point.
In step S124, in response to the position of the current suspected feature point being greater than the first threshold from the position of the previous feature point, the current suspected feature point is confirmed to be the feature point.
Specifically, the current suspected feature point is considered to be a non-feature point in response to the position of the current suspected feature point being not greater than a first threshold from the position of the previous feature point. In the previous step, if the unmanned aerial vehicle flies forward along the obstacle and does not hover, the recorded data is valid data. However, although the unmanned aerial vehicle may fly forward along the obstacle, the condition of spiraling or turning at a certain moment may be assumed that the identification point still satisfies the temporary storage condition, but the temporary storage data is invalid in practice, so that it needs to be determined that the position of the current suspected feature point is greater than the first threshold from the position of the previous suspected feature point, if yes, it indicates that the unmanned aerial vehicle is not spiraling or turning during the flight, if no, it indicates that the condition of spiraling or turning may occur, and the suspected feature point at this time is actually a non-feature point and needs to be discarded.
In step S125, in response to the number of feature points reaching the fourth threshold, the positions of the feature points are acquired.
In particular, the fourth threshold may be dependent on the contour of the particular obstacle or the accuracy of the three-dimensional model of the obstacle to be constructed.
As shown in fig. 3, in one embodiment, constructing a three-dimensional model of an obstacle calibrated with position data based on a plurality of feature points and positions of the feature points, includes the steps of:
Step S131, selecting characteristic points from a plurality of characteristic points to construct a convex polyhedron.
Specifically, obtaining a feature point with the largest height among a plurality of feature points; the intersection point of the vertical line and the ground is obtained by making the vertical line from the feature point with the maximum height to the ground; selecting three feature points farthest from the intersection point from the feature points except the feature point with the largest height; constructing an initial convex polyhedron based on the feature point with the largest height and the three feature points; and sequentially adding a characteristic point based on the initial convex polyhedron to form a new convex polyhedron until all the characteristic points are added to obtain a final convex polyhedron.
For an aircraft to avoid an obstacle, if the obstacle is represented by a concave polyhedron, the concave area may be considered by the aircraft as free flight space, which actually creates a hidden danger for the aircraft. And therefore is better represented by a convex polyhedron. Therefore, only the constituent convex polyhedrons are selected in the selection of the feature points.
The specific method comprises the following steps: first, a feature point (point a) having the greatest height is selected, and then a center point is determined from the point as a normal line perpendicular to the ground. About the center point, 4 feature points (B, C, D are not necessarily on the same plane) farthest from the center point are determined, and A, B, C, D constitutes an initial convex polyhedron.
Taking the initial convex polyhedron as the current convex polyhedron, sequentially adding a characteristic point to form a new polyhedron. And judging whether the new polyhedron is still a convex polyhedron, and taking the new polyhedron as the current convex polyhedron. If the new polyhedron is no longer a convex polyhedron, the newly added feature points are removed, and the current convex polyhedron is kept unchanged. And then adding other feature points until all feature points are calculated.
It is known to determine whether or not a polyhedron composed of a plurality of feature points is a convex polyhedron. The basic principle is as follows: let the newly generated polyhedron be a convex polyhedron, let the face CiCjCk (i, j, k=1 to 7, and i not equal to j not equal to k) be any one face of the new polyhedron, ci, cj, ck be 3 vertices of the face respectively, cp (p=1 to 7, p not equal to i, j, k) be any vertex other than the 3 vertices, when the polyhedron is a convex polyhedron, the points Cp must be located on the same side of the face CiCjCk. Therefore, vi is the external normal vector of the surface CiCjCk, vip is the vector directed to the point Cp through the point Ci, and the value of vi·vip is negative according to the vector algorithm.
Step S132, discarding feature points falling inside the convex polyhedron.
Step S133, obtaining the obstacle three-dimensional model calibrated with the position data based on the positions of the feature points of the constructed convex polyhedron.
In one embodiment, the obstacle calibration method further comprises the steps of:
And connecting the characteristic points of the two obstacle three-dimensional models to form a new obstacle three-dimensional model in response to the distance between any characteristic point on the obstacle three-dimensional model and any characteristic point on the other obstacle three-dimensional model being smaller than a fifth threshold value.
Specifically, because of the large aircraft size, if multiple obstacles are very close together (a threshold slightly larger than the aircraft size is set, and if the distance between any two feature points belonging to two obstacles is smaller than the threshold, joint calibration is considered to be needed), the obstacles can be treated as one obstacle. Namely: all the characteristic points of the two obstacles are placed together, and the characteristic points are selected according to the method to form a convex polyhedron, so that the reliability of obstacle avoidance of the aircraft is reduced, and the computational complexity is reduced.
As shown in fig. 4, 5 and 6, in a specific embodiment, the unmanned aerial vehicle is operated to calibrate the obstacle to obtain a moving track, see fig. 4 and 5, where E-W in fig. 5 represents east-west longitude, N-S represents north latitude-south latitude, and U-D represents high-low. And extracting characteristic points according to the unmanned aerial vehicle track to obtain the characteristic points of the obstacle. At least 8 characteristic points are obtained from the unmanned aerial vehicle flight track, and longitude, latitude and altitude of the at least 8 characteristic points form 8 points to be modeled and stored as obstacle profile data.
The concrete explanation is as follows:
1. And running a feature point extraction program, reading in the unmanned aerial vehicle flight track file, and storing the position information.
2. It is determined whether the drone is moving, i.e. whether the current position (longitude, latitude, altitude) differs from the position of the last second (longitude, latitude, altitude) by more than 50cm.
3. If the number of the temporary storage identification points is larger than 50cm, the position of the temporary storage current moment is the identification point, the number k of the temporary storage identification points is increased by 1, and if the number of the temporary storage identification points is smaller than 50cm, temporary storage data are emptied.
4. If the number k of the temporary storage identification points is smaller than 10, returning to the step 1, and continuing to judge the next moment. If the number k of temporary storage identification points is equal to 10, marking the identification points as suspected feature points.
5. And judging whether the current suspected feature point and the last feature point are at the same position or not, namely judging whether the position (longitude, latitude and altitude) of the current suspected feature point and the position (longitude, latitude and altitude) of the last feature point are different by 50cm or not.
6. If the current suspected feature point and the last feature point are at the same position, discarding the current suspected feature point, returning to the step 1, and continuing to judge the next moment. If the current suspected feature point and the last feature point are not in the same position, the current suspected feature point is saved as the feature point, and the number of the feature points is increased by 1.
7. And judging whether the number of the feature points reaches 8. If the number of the feature points is less than 8, returning to the step 1, and continuing to judge the next moment. If the number of the characteristic points reaches 8, the cycle is ended, and the longitude and latitude height data of 8 characteristic points are obtained.
8. Among the obtained feature points, feature points are selected to form a convex polyhedron, and other feature points are discarded, and fig. 5 is a rectangular parallelepiped formed of 8 feature points obtained through feature point extraction.
9. If multiple obstacles exist, joint calibration of the multiple obstacles is further performed (i.e. merging to generate one large obstacle).
10. The feature point codes are broadcasted, eight feature points are coded according to a certain rule, and the coding example is as follows:
$BJ,181,ZBBB,1,0,0,28.8218955617,N,115.9433355067,E,53.6390000014,28.8219728067,N,115.9433355067,E,53.6390000014,28.8219728067,N,115.9434218233,E,53.6390000014,28.8218955617,N,115.9434218233,E,53.6390000014,28.8218955617,N,115.9434218233,E,52.0529999994,28.8219728067,N,115.9434218233,E,52.0529999994,28.8219728067,N,115.9433355067,E,52.0529999994,28.8218955617,N,115.9433355067,E,52.0529999994,A,22,3,2,10,40,38*57.
The unmanned aerial vehicle is operated to calibrate the low-altitude obstacle and the characteristic point extraction algorithm, so that the problem that the low-altitude obstacle is too high and difficult to measure is solved, the position calibration precision of the low-altitude obstacle is improved, and the problem of low-altitude obstacle position data storage and transmission is solved through data coding.
The obstacle calibration device provided by the invention is described below, and the obstacle calibration device described below and the obstacle calibration method described above can be referred to correspondingly.
As shown in fig. 7, in one embodiment, an obstacle alignment apparatus includes:
The reading module 710 is configured to read a moving track of the mobile station for calibrating the obstacle, and a position of the moving track, where the position includes a longitude, a latitude and a height, and the moving track is a centimeter-level precision flight track obtained by an airborne satellite navigation RTK when the unmanned aerial vehicle flies around an external contour of the obstacle. ;
An extracting module 720, configured to extract a plurality of feature points along the moving track, where an interval between the plurality of feature points is greater than a first threshold;
a construction module 730 is configured to construct a three-dimensional model of the obstacle calibrated with the position data based on the plurality of feature points.
In this embodiment, the extraction module 720 is specifically configured to:
Selecting an identification point along the running track at each interval of preset time length;
In response to the position of the current identification point being greater than the second threshold from the position of the previous identification point, temporarily storing the current identification point;
When the number of the temporarily stored identification points reaches a third threshold value, marking the currently temporarily stored identification points as suspected feature points;
And confirming the current suspected feature point as the feature point in response to the fact that the position of the current suspected feature point is greater than a first threshold from the position of the previous feature point.
In this embodiment, the extraction module 720 is specifically further configured to:
and acquiring the position of each characteristic point in response to the number of the characteristic points reaching a fourth threshold.
In this embodiment, the extraction module 720 is specifically further configured to:
Clearing the temporarily stored identification point in response to the position of the current identification point being not greater than a second threshold from the position of the previous identification point;
and in response to the position of the current suspected feature point being not greater than a first threshold from the position of the previous feature point, identifying the current suspected feature point as a non-feature point.
In this embodiment, the construction module 730 is specifically configured to:
Selecting characteristic points from a plurality of characteristic points to construct a convex polyhedron;
Discarding the characteristic points falling into the convex polyhedron;
And obtaining the obstacle three-dimensional model calibrated with the position data based on the positions of the feature points of the constructed convex polyhedron.
In this embodiment, the construction module 730 is specifically further configured to:
acquiring a feature point with the largest height from the feature points;
The intersection point of the vertical line and the ground is obtained by making the vertical line from the feature point with the maximum height to the ground;
selecting three feature points farthest from the intersection point from the feature points except the feature point with the largest height;
Constructing an initial convex polyhedron based on the feature point with the largest height and the three feature points;
And sequentially adding a characteristic point based on the initial convex polyhedron to form a new convex polyhedron until all the characteristic points are added to obtain a final convex polyhedron.
In this embodiment, the obstacle calibration device further includes:
And the response module is used for connecting the characteristic points of the two obstacle three-dimensional models to form a new obstacle three-dimensional model in response to the fact that the distance between any characteristic point on the obstacle three-dimensional model and any characteristic point on the other obstacle three-dimensional model is smaller than a fifth threshold value.
According to the obstacle calibration device, the moving track and the position of the moving track of the moving station for calibrating the obstacle are read, a plurality of characteristic points with intervals larger than the first threshold value are extracted from the moving track, then the extracted characteristic points are utilized to construct an obstacle three-dimensional model with obstacle position data for calibration, the position data in the obstacle three-dimensional model can reflect the actual longitude, latitude and height of the obstacle and the outline characteristics of the obstacle, so that the obstacle information can be provided for a high-resolution remote sensing map sufficiently, the accurate measurement of the obstacle is realized, and the requirements of a general aviation safety flight scene are met.
Fig. 8 illustrates a physical structure diagram of an electronic device, which may be an intelligent terminal, and an internal structure diagram thereof may be as shown in fig. 8. The electronic device includes a processor, a memory, and a network interface connected by a system bus. Wherein the processor of the electronic device is configured to provide computing and control capabilities. The memory of the electronic device includes a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The network interface of the electronic device is used for communicating with an external terminal through a network connection. The computer program, when executed by a processor, implements a method of obstacle calibration, the method comprising:
Reading a moving track of the mobile station for calibrating the obstacle and the position of the moving track, wherein the position comprises longitude, latitude and height, the mobile station is an unmanned aerial vehicle, and the moving track is a centimeter-level precision flying track obtained by an onboard satellite navigation RTK when the unmanned aerial vehicle flies around the outer contour of the obstacle. ;
Extracting a plurality of feature points along the running track, wherein the intervals among the feature points are larger than a first threshold value;
And constructing a three-dimensional obstacle model calibrated with position data based on the plurality of characteristic points.
It will be appreciated by those skilled in the art that the structure shown in fig. 8 is merely a block diagram of a portion of the structure associated with the present inventive arrangements and is not limiting of the electronic device to which the present inventive arrangements are applied, and that a particular electronic device may include more or fewer components than shown, or may combine certain components, or have a different arrangement of components.
In another aspect, the present invention also provides a computer storage medium storing a computer program which when executed by a processor implements a method for calibrating an obstacle, the method comprising:
Reading a moving track of the mobile station for calibrating the obstacle and the position of the moving track, wherein the position comprises longitude, latitude and height, the mobile station is an unmanned aerial vehicle, and the moving track is a centimeter-level precision flying track obtained by an onboard satellite navigation RTK when the unmanned aerial vehicle flies around the outer contour of the obstacle;
Extracting a plurality of feature points along the running track, wherein the intervals among the feature points are larger than a first threshold value;
And constructing a three-dimensional obstacle model calibrated with position data based on the plurality of characteristic points.
In yet another aspect, a computer program product or computer program is provided, the computer program product or computer program comprising computer instructions stored in a computer readable storage medium. A processor of an electronic device reads the computer instructions from a computer readable storage medium, the processor executing the computer instructions to implement a method of obstacle calibration, the method comprising:
Reading a moving track of the mobile station for calibrating the obstacle and the position of the moving track, wherein the position comprises longitude, latitude and height, the mobile station is an unmanned aerial vehicle, and the moving track is a centimeter-level precision flying track obtained by an onboard satellite navigation RTK when the unmanned aerial vehicle flies around the outer contour of the obstacle;
Extracting a plurality of feature points along the running track, wherein the intervals among the feature points are larger than a first threshold value;
And constructing a three-dimensional obstacle model calibrated with position data based on the plurality of characteristic points.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in embodiments provided herein may include non-volatile and/or volatile memory. The nonvolatile memory can include Read Only Memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory.
By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (SYNCHLINK) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), among others.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The foregoing examples illustrate only a few embodiments of the invention and are described in detail herein without thereby limiting the scope of the invention. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the invention, which are all within the scope of the invention. Accordingly, the scope of protection of the present invention is to be determined by the appended claims.
Claims (9)
1. A method of calibrating an obstacle, the method comprising:
reading a moving track of a mobile station for calibrating an obstacle and a position of the moving track, wherein the position comprises longitude, latitude and height, the mobile station is an unmanned aerial vehicle, the unmanned aerial vehicle flies through a plurality of points capable of reflecting the external contour of the obstacle, and the moving track is a centimeter-level precision flying track obtained by an onboard satellite navigation RTK when the unmanned aerial vehicle flies around the external contour of the obstacle;
Extracting a plurality of characteristic points along the running track, wherein the intervals among the plurality of characteristic points are larger than a first threshold value;
constructing an obstacle three-dimensional model calibrated with position data based on the plurality of characteristic points, wherein the construction of an obstacle outline is completed according to the extracted plurality of characteristic points, and then the actual longitude, latitude and height of the obstacle are known according to the positions of the characteristic points on the obstacle outline;
the extracting a plurality of feature points along the running track includes:
selecting an identification point along the running track at each interval of preset time length;
in response to the position of the current identification point being greater than a second threshold from the position of the previous identification point, temporarily storing the current identification point;
When the number of the temporarily stored identification points reaches a third threshold value, marking the currently temporarily stored identification points as suspected feature points;
And confirming that the current suspected feature point is a feature point in response to the fact that the position of the current suspected feature point is greater than the first threshold from the position of the previous feature point.
2. The obstacle alignment method according to claim 1, wherein the confirming that the current suspected feature point is a feature point, then comprises:
and acquiring the position of each characteristic point in response to the number of the characteristic points reaching a fourth threshold.
3. The obstacle alignment method according to claim 1 or 2, wherein the extracting a plurality of feature points along the moving trajectory further comprises:
Clearing the temporarily stored identification point in response to the position of the current identification point being not greater than the second threshold value from the position of the previous identification point;
And in response to the fact that the position of the current suspected feature point is not greater than the first threshold value from the position of the previous feature point, the current suspected feature point is considered to be a non-feature point.
4. The obstacle calibration method according to claim 1, wherein the constructing an obstacle three-dimensional model calibrated with position data based on the plurality of feature points includes:
selecting characteristic points from the plurality of characteristic points to construct a convex polyhedron;
discarding feature points falling inside the convex polyhedron;
and obtaining the three-dimensional obstacle model calibrated with the position data based on the positions of the characteristic points of the constructed convex polyhedron.
5. The obstacle alignment method as claimed in claim 4, wherein selecting a feature point among the plurality of feature points to construct a convex polyhedron comprises:
acquiring a feature point with the largest height from the feature points;
the intersection point of the vertical line and the ground is obtained by making the vertical line from the feature point with the maximum height to the ground;
selecting three feature points farthest from the intersection point from the feature points except the feature point with the largest height;
constructing an initial convex polyhedron based on the feature point with the largest height and the three feature points;
And sequentially adding a characteristic point based on the initial convex polyhedron to form a new convex polyhedron until all the characteristic points are added to obtain a final convex polyhedron.
6. The obstacle alignment method as claimed in claim 1, further comprising:
And connecting the characteristic points of the two obstacle three-dimensional models to form a new obstacle three-dimensional model in response to the distance between any characteristic point on the obstacle three-dimensional model and any characteristic point on the other obstacle three-dimensional model being smaller than a fifth threshold value.
7. An obstacle alignment device, the device comprising:
The mobile station is an unmanned aerial vehicle, the unmanned aerial vehicle flies through a plurality of points capable of reflecting the external outline of the obstacle, and the moving track is a centimeter-level flight track obtained by an onboard satellite navigation RTK when the unmanned aerial vehicle flies around the external outline of the obstacle;
The extraction module is used for extracting a plurality of characteristic points along the running track, and the intervals among the characteristic points are larger than a first threshold value;
the construction module is used for constructing an obstacle three-dimensional model marked with position data based on the plurality of characteristic points and the positions of the characteristic points, wherein the construction of the outline of the obstacle is completed according to the extracted plurality of characteristic points, and the actual longitude, latitude and height of the obstacle are obtained according to the positions of the characteristic points on the outline of the obstacle;
the extracting a plurality of feature points along the running track includes:
selecting an identification point along the running track at each interval of preset time length;
in response to the position of the current identification point being greater than a second threshold from the position of the previous identification point, temporarily storing the current identification point;
When the number of the temporarily stored identification points reaches a third threshold value, marking the currently temporarily stored identification points as suspected feature points;
And confirming that the current suspected feature point is a feature point in response to the fact that the position of the current suspected feature point is greater than the first threshold from the position of the previous feature point.
8. An electronic device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any one of claims 1 to 6 when the computer program is executed.
9. A computer readable storage medium storing a computer program, characterized in that the computer program when executed by a processor implements the steps of the method of any one of claims 1 to 6.
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